1. Location of Severe Fires

Provide a leaflet map of the highest severity fires (i.e. subset to the highest category in HIGHEST_LEVEL_DESC) contained in the file buiding_fires.csv. Ignore locations that fall outside the five boroughs of New York City. Provide at least three pieces of information on the incident in a popup.

Below, the map shows the most severe fires across the five boroughs of New York city. The list below shows that the dataset is filtered for only values that fall into any of the boroughs and others are ignored.

## [1] "4 - Brooklyn"      "2 - Bronx"         "5 - Queens"       
## [4] "1 - Manhattan"     "3 - Staten Island"

2. Layers and Clusters

a) Color by Type of Property

Start with the previous map. Now, distinguish the markers of the fire locations by PROPERTY_USE_DESC, i.e. what kind of property was affected. If there are too many categories, collapse some categories. Choose an appropriate coloring scheme to map the locations by type of affected property. Add a legend informing the user about the color scheme. Also make sure that the information about the type of affected property is now contained in the popup information. Show this map.

b) Cluster

Add marker clustering, so that zooming in will reveal the individual locations but the zoomed out map only shows the clusters. Show the map with clusters.

3. Fire Houses

The second data file contains the locations of the 218 firehouses in New York City. Start with the non-clustered map (2a) and now adjust the size of the circle markers by severity (TOTAL_INCIDENT_DURATION or UNITS_ONSCENE seem plausible options). More severe incidents should have larger circles on the map. On the map, also add the locations of the fire houses. Add two layers (“Incidents”, “Firehouses”) that allow the user to select which information to show.

4. Distance from Firehouse and Response Time

We now want to investigate whether the distance of the incident from the nearest firehouse varies across the city.

a) Calculate Distance

For all incident locations (independent of severity), identify the nearest firehouse and calculate the distance between the firehouse and the incident location. Provide a scatter plot showing the time until the first engine arrived (the variables INCIDENT_DATE_TIME and ARRIVAL_DATE_TIME) will be helpful.

Now also visualize the patterns separately for severe and non-severe incidents (use HIGHEST_LEVEL_DESC but feel free to reduce the number of categories). What do you find?

The graph above shows that there is a slight positive relationship between the distance from the nearest firehouse and the time between the incident happened and the fire fighters arrived. However, outlier values are mostly belong to the first alarm category or to the final alarm. Looking at the final alarm outliers, it is visible that these outliers are closer to firehouses location-wise, yet still have a long response time. But the spread of the pink dots is wider vertically around the left side of the graph. On the other hand, some pink doth indicating that the location of the fire is distant from the fire station still show low response times.

b) Map of Response Times

Provide a map visualization of response times. Investigate whether the type of property affected (PROPERTY_USE_DESC) or fire severity (HIGHEST_LEVEL_DESC) play a role here.

The below map shows the relationship between

  1. time between incident and arrival of help and the type of property

  2. time between incident and arrival of help and the severity of the fire

The map is presented in two layers and popups are provided for the type of property and severity of the fire. The use of this map is best to look at the largest plots of the map showing that the help that arrived to the fire was slow and look at what the popups show for these spots.

The first layer (1) shows that the longest it took for fire fighters to arrive in the midtown and financial district area and the largest circles appear for office buildings and for hotels mostly. (The largest circle stands actually stands for an unspecified place.)

The second layer shows that for these large circles on the map, there has been only one alarm.